Security and Privacy in Agentic Systems
Security and Privacy in Agentic Systems is an intensive one-day experience crafted for AI engineers, security architects, and privacy professionals who must safeguard autonomous AI deployments. Participants explore foundational principles—such as stringent identity and permission management, robust encryption, and comprehensive logging frameworks—that establish trust in every action an AI agent performs. The course highlights the unique failure modes in single-agent and multi-agent LLM environments, demonstrating how prompt injection, model misbehavior, and coordination breakdowns can cascade into real-world vulnerabilities. Drawing on leading standards and cutting-edge privacy-enhancing technologies like differential privacy and homomorphic encryption, attendees learn to design secure architectures and sandboxed workflows that protect sensitive data at each processing stage. Through practical labs designed to give participants hands-on experience in securing agentic AI systems, professionals will develop the skills needed to anticipate threats, harden agent interactions, and maintain compliance in rapidly evolving regulatory landscapes.
Upon completing this course, participants will be able to:
- Analyze core security and privacy challenges in agentic AI, from transparency gaps to data exposure
- Evaluate vulnerabilities in single-agent LLM deployments and apply targeted mitigation strategies
- Secure multi-agent LLM workflows against coordination failures and adversarial attacks
- Leverage privacy-enhancing technologies to protect sensitive data in autonomous AI pipelines
Other Dates
Oct 2026
05
San Francisco
Jan 2027
15
Barcelona